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C code for packet loss simulator

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Jean-Marc Valin 2023-12-21 21:30:53 -05:00
parent b923fd1e28
commit bd710e97f3
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4 changed files with 101 additions and 1 deletions
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69
dnn/lossgen.c Normal file
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@ -0,0 +1,69 @@
#ifdef HAVE_CONFIG_H
#include "config.h"
#endif
#include <math.h>
#include "lossgen.h"
#include "os_support.h"
#include "nnet.h"
#include "lpcnet_private.h"
int sample_loss(
LossGenState *st,
float percent_loss,
int arch
)
{
float input[2];
float tmp[LOSSGEN_DENSE_IN_OUT_SIZE];
float out;
int loss;
LossGen *model = &st->model;
input[0] = st->last_loss;
input[1] = percent_loss;
compute_generic_dense(&model->lossgen_dense_in, tmp, input, ACTIVATION_TANH, arch);
compute_generic_gru(&model->lossgen_gru1_input, &model->lossgen_gru1_recurrent, st->gru1_state, tmp, arch);
compute_generic_gru(&model->lossgen_gru2_input, &model->lossgen_gru2_recurrent, st->gru2_state, st->gru1_state, arch);
compute_generic_dense(&model->lossgen_dense_out, &out, st->gru2_state, ACTIVATION_SIGMOID, arch);
loss = (float)rand()/RAND_MAX < out;
st->last_loss = loss;
return loss;
}
void lossgen_init(LossGenState *st)
{
int ret;
OPUS_CLEAR(st, 1);
#ifndef USE_WEIGHTS_FILE
ret = init_lossgen(&st->model, lossgen_arrays);
#else
ret = 0;
#endif
celt_assert(ret == 0);
}
int lossgen_load_model(LossGenState *st, const unsigned char *data, int len) {
WeightArray *list;
int ret;
parse_weights(&list, data, len);
ret = init_lossgen(&st->model, list);
opus_free(list);
if (ret == 0) return 0;
else return -1;
}
#if 0
#include <stdio.h>
int main(int argc, char **argv) {
int i, N;
float p;
LossGenState st;
lossgen_init(&st);
p = atof(argv[1]);
N = atoi(argv[2]);
for (i=0;i<N;i++) {
printf("%d\n", sample_loss(&st, p, 0));
}
}
#endif

30
dnn/lossgen.h Normal file
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@ -0,0 +1,30 @@
#ifndef LOSSGEN_H
#define LOSSGEN_H
#include "lossgen_data.h"
#define PITCH_MIN_PERIOD 32
#define PITCH_MAX_PERIOD 256
#define NB_XCORR_FEATURES (PITCH_MAX_PERIOD-PITCH_MIN_PERIOD)
typedef struct {
LossGen model;
float gru1_state[LOSSGEN_GRU1_STATE_SIZE];
float gru2_state[LOSSGEN_GRU2_STATE_SIZE];
int last_loss;
} LossGenState;
void lossgen_init(LossGenState *st);
int lossgen_load_model(LossGenState *st, const unsigned char *data, int len);
int sample_loss(
LossGenState *st,
float percent_loss,
int arch
);
#endif

1
dnn/nnet.h
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@ -147,6 +147,7 @@ extern const WeightArray rdovaedec_arrays[];
extern const WeightArray fwgan_arrays[];
extern const WeightArray fargan_arrays[];
extern const WeightArray pitchdnn_arrays[];
extern const WeightArray lossgen_arrays[];
int linear_init(LinearLayer *layer, const WeightArray *arrays,
const char *bias,

2
dnn/torch/lossgen/README.md
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@ -7,7 +7,7 @@ to build a generative model for packet loss.
We use the training data provided for the Audio Deep Packet Loss Concealment Challenge, which is available at:
http://plcchallenge2022pub.blob.core.windows.net/plcchallengearchive/test\_train.tar.gz
http://plcchallenge2022pub.blob.core.windows.net/plcchallengearchive/test_train.tar.gz
To create the training data, run: